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* build: ensure all regional bedrock models have same supported values as base bedrock model prevents drift * test(base_llm_unit_tests.py): add testing for nested pydantic objects * fix(test_utils.py): add test_get_potential_model_names * fix(anthropic/chat/transformation.py): support nested pydantic objects Fixes https://github.com/BerriAI/litellm/issues/7755
67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
from abc import ABC, abstractmethod
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from typing import List, Optional, Type, Union
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from openai.lib import _parsing, _pydantic
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from pydantic import BaseModel
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from litellm.types.utils import ModelInfoBase
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class BaseLLMModelInfo(ABC):
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@abstractmethod
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def get_model_info(
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self,
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model: str,
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existing_model_info: Optional[ModelInfoBase] = None,
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) -> Optional[ModelInfoBase]:
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pass
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@abstractmethod
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def get_models(self) -> List[str]:
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pass
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@staticmethod
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@abstractmethod
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def get_api_key(api_key: Optional[str] = None) -> Optional[str]:
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pass
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@staticmethod
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@abstractmethod
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def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
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pass
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def type_to_response_format_param(
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response_format: Optional[Union[Type[BaseModel], dict]],
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ref_template: Optional[str] = None,
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) -> Optional[dict]:
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"""
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Re-implementation of openai's 'type_to_response_format_param' function
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Used for converting pydantic object to api schema.
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"""
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if response_format is None:
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return None
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if isinstance(response_format, dict):
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return response_format
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# type checkers don't narrow the negation of a `TypeGuard` as it isn't
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# a safe default behaviour but we know that at this point the `response_format`
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# can only be a `type`
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if not _parsing._completions.is_basemodel_type(response_format):
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raise TypeError(f"Unsupported response_format type - {response_format}")
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if ref_template is not None:
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schema = response_format.model_json_schema(ref_template=ref_template)
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else:
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schema = _pydantic.to_strict_json_schema(response_format)
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return {
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"type": "json_schema",
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"json_schema": {
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"schema": schema,
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"name": response_format.__name__,
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"strict": True,
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},
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}
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